An ideal drug cures a decease and does not kill a patient (or even lab animals in the course of preclinical testing). Usual drug discovery paradigm is based on studying a compound's properties against a specific, normally decease-related (protein) target. The ability of a compound to bind (inhibit) a specific target is called efficacy.
Even if the efficacy is good, another important property of a compound is its toxicity. Toxicity is related to the compound physical properties, such as solubility etc, as well by its ability to bind to and hence inhibit various vital human proteins (and may be even DNA and RNA).
Common sense suggests that an ideal compound binds its specific drug related target and does not bind to anything else. Anything in between is toxic, at least to a some extent. For example, most of important properties utilize ATP molecules, which means that human body contains a lot of ATP-bindig proteins. If you make a drug attacking an ATP-binding site of a "bad" protein, most probably, a lot of "good" and useful proteins will be also affected. In that case your compound should be toxic. This is indeed the case for many cancer drugs attacking ATP-binding sites of kinases.
The latter statement is the foundation of our approach. Although it's quite conceptually simple, it's useless unless it can be supplemented by a meaningful mathematical model. Let us dwell into some more details to see how the whole thing can be made working.
Let us overview important properties of a drug candidate. First there is a bunch of physical properties, such as solubility, differential solubility, LogP (namely the difference between water and lipid solubility) etc. These quantities are easy to measure, are of direct physical meaning and can be pretty easily calculated (with or without QUANTUM software).
Another set of characteristics defines a compound ability to penetrate through cell membranes and its biochemical in liver. These are quantities deturmining bioavailability, half life, volume of distribution etc. None of such quantities can be evaluated using the simple physical properties alone. For example, drug absorbtion depends on the molecule interaction with proteins actively transporting the molecules through the cell membranes.
The bottom line: bioavailability and other quantities require understanding of a compound binding properties to a selected number of proteins participating in a compound transport and metabolism.
So the conclusion is that IF YOU KNOW WHICH PROTEINS ARE IMPORTANT, AND IF YOU CAN CALCULATE HOW YOUR COMPOUND BINDS TO THEM, YOU KNOW THE COMPOUND PHARMACOLOGICAL AND TOXICOLOGICAL PROPERTIES
Now the only problem how to identify those "important" proteins.
Fortunately, there are thousands of molecules with known properties. What we can do is the following:
- take a molecule
- calculate its binding to any human protein with known 3d structure
- use the obtained binding affinities (numbers) as a molecule's binding profile fingerprint (the Biological Spectrum), characterizing the ability of the molecule to interact with the whole human proteome
Now assume we know such Biological Spectra for 1000s molecules with well known properties. This means we can now datamine the fingerprints->known properites relations. The basic premise is, of course, that the molecules with similar fingerprints have similar properties.
We have a number of proofs of such technology working. The most recent one is the prediction of active transport drug absorption properties for drug like molecues based on the binding data against human brain hexokinase type I-related protein. We prove that the binding energy of a compound against the protein may serve to distinguish between the passively and actively transported molecules and even help to calculated the drug absorbtion quantitatevely.
Thursday, September 25, 2008
From binding data to pharmacokinetics: a novel approach to active drug absorbtion prediction
Oral administered drugs are mainly absorbed in the small intestine. Here, depending on drug composition and size, absorption can happen through a variety of processes . Through the epithelial cells and the lamina pro- pria the drug passes from the lumen into the blood stream in the capillaries. On its way it might be metabolised, transported away from the tract where absorption is possible or accumulate in organs other than those of treatment. Besides a fundamental interest in understanding the basic mechanisms by which a drug is assimilated by the human body, the kinetics of drug absorption is also a topic of much practical interest. A detailed knowledge of this process, resulting in the prediction of the drug absorption profile, can be of much help in the drug development stage .
To this end, several kinetic models for drug absorption within the body have been introduced (see e.g. ). They necessarily introduce some simplifications belonging to the category of the so-called three-compartment models where the substances (such as drugs or nutrients) move between three volumes (e.g. the human organs). In fact the models require two kinds of molecular properties. First are purely physical characteristics, such as solubility, differential solubility, LogP etc. These quantities are easy to measure or to calcualte, have direct physical meaning and sufficient to predict absorbtion profile of passively absorbed drugs. Actively transported molecules interact with protein transporters and therefore prediction for actively transporting compounds require a lot of separate knowledge of binding to and kinetics of the transporting proteins.
The major objective of this investigation was to develop a drug absorbtion prediction approach based on entirely different paradigm, thus avoiding difficulties of both knowledge-based and QSAR-based models, and therefore capable of better predictions. Recently it was observed that experimental values of molecular activities against a large proteins set can be used for predicting broad biological effects . In this investigation we take advantage of this concept and develop a novel quntitative method for identification of actively transported drugs. To do that we performed a docking study of a few hundreds small molecules (mostly drugs) against a diversified 510 proteins set representing human proteom. Using available absorbtion data for each of the molecules we obtained a support vector classifier capable to identify proteins which affinity for drugs correlates well with the active absorption of these drugs in 81% cases. The observation helped us improve our passive absorbtion model by adding non-liner fluxes associated with the transporting protein to obtain also a quantitative model of the passively absorbed drugs.
Ref: arXiv:0810.2617 [ps, pdf, other]
To this end, several kinetic models for drug absorption within the body have been introduced (see e.g. ). They necessarily introduce some simplifications belonging to the category of the so-called three-compartment models where the substances (such as drugs or nutrients) move between three volumes (e.g. the human organs). In fact the models require two kinds of molecular properties. First are purely physical characteristics, such as solubility, differential solubility, LogP etc. These quantities are easy to measure or to calcualte, have direct physical meaning and sufficient to predict absorbtion profile of passively absorbed drugs. Actively transported molecules interact with protein transporters and therefore prediction for actively transporting compounds require a lot of separate knowledge of binding to and kinetics of the transporting proteins.
The major objective of this investigation was to develop a drug absorbtion prediction approach based on entirely different paradigm, thus avoiding difficulties of both knowledge-based and QSAR-based models, and therefore capable of better predictions. Recently it was observed that experimental values of molecular activities against a large proteins set can be used for predicting broad biological effects . In this investigation we take advantage of this concept and develop a novel quntitative method for identification of actively transported drugs. To do that we performed a docking study of a few hundreds small molecules (mostly drugs) against a diversified 510 proteins set representing human proteom. Using available absorbtion data for each of the molecules we obtained a support vector classifier capable to identify proteins which affinity for drugs correlates well with the active absorption of these drugs in 81% cases. The observation helped us improve our passive absorbtion model by adding non-liner fluxes associated with the transporting protein to obtain also a quantitative model of the passively absorbed drugs.
Ref: arXiv:0810.2617 [ps, pdf, other]
The nature of percolation phase transition in films of hydration water around immersed bodies.
In a set of molecular dynamics calculations (MD) the percolation phase transition in water layer absorbed on a body surface was revealed at definite temperature. Below this temperature the infinite network of unbroken hydrogen bonds exists. Above it this network decays on islands. This conclusion corresponds also with measurements of conduction of moisture contained disperse materials as quartz, for example: the conductivity drops almost to zero value while heating the specimens up to definite temperature. It is known that the water conductance dominates by the “estafette” mechanism in which protons are transferred over the hydrogen bonds. The breakdown of network means the conductivity drop. These phenomena are explained in the paper in frames of early published continuous vector model of polar liquids. It is shown that the immersed bodies are surrounded by the ferroelectric film, in which the dipole moments of water molecules are ordered, arranged in one direction parallel to the interface. It is the physics behind above mentioned MD results. In addition of our previous papers the stability of this ferroelectric order is proved. The character of phase transition to the paraelectric phase is discussed and its temperature is estimated that is in agreement with MD results. Below the critical temperature the polarization vector field contains the structures as “vortex-antivortex pairs”. These pairs dissociate above this temperature that means the order breaking. The boundary conditions for the polarization vector field of molecular dipole moments are derived that is necessary to enclose the vector model equations.Reference: accepted for publication to Journal of Structual Chemistry (Russian Journal of), 2008
Spontaneous polarization of a polar liquid next to nano-scale impurities
Numerous properties of water are determined by the hydrogen bonds between its molecules. Water does not form hydrogen bonds with hydrophobic materials, henceforth, dipole moments of its molecules are arranged mainly parallel to the interfaces with such substances. According to molecular dynamics calculations (MD) at such orientation molecules save the maximal number of hydrogen bonds: three of fourth. It is shown in this Letter that in the layer of water or ice next to surface the long-range order spontaneously forms: remaining parallel to the surface dipole moment vectors arrange in one direction. Some fraction of dipole moments form the vortex structures on the surface. At low temperatures the ordered state has small admixture of vortex-antivortex pairs. The interaction energy of vortexes in this pairs arises proportional to the distance between them. A definite temperature the phase transition takes place: pairs suffer the dissociation, the molecular dipole moments order disappears. This conclusion agrees with he results of MD calculations, in which the percolation phase transition was revealed in the hydrogen bond network of water molecules absorbed on a surface. The spontaneous polarization of liquid induced by the immersed in it nano-size bodies (proteins, peptides, …) results in the additional long-range interaction between them that depends on their relative orientation. Polarization of liquid in this case looks like that presented in Fig.1 in agreement with MD. All mentioned MD results can not be explained in frames of standard continuous scalar theory of water. These phenomena were analyzed here in frames of continuous vector model of polar liquids applications of which looks like promising to speed the simulations of macromolecular complexes.
Reference: arXiv:cond-mat/0601129 [ps, pdf, other]
Accepted for publication in Journal of Physical Chemistry A (Russian Journal of), 2009
What's an ultimate value of reversible drug binding constant?
Traditional opinion is that a good drug must have a high value of the absolute meaning of the binding energy with target protein in order to prevent the thermal dissociation of the drug-protein complex. In this case an essential deformation of protein arises, which has to be taken into account in developing different models of protein-small molecule and protein-protein interaction, and computing affinity constants in drug discovery in-silico methods. The effect of essential perturbation of protein molecule is ignored in standard computational methods of drug design that can contribute a large mistake to results of calculation, to binding energy, for example.To demonstrate the existence of the ultimate value of the binding energy two models are considered: macroscopic and microscopic, both giving the same conclusions: the critical value of absolute meaning of binding energy is 50-100kJ/M. If the binding energy exceeds this value, then drug essentially perturbs protein configuration. In a microscopic picture this perturbation is a sequence of irreversible conformational transitions in protein body. In a macroscopic one it is an inelastic deformation of a protein substance. Our estimation agrees with the experimental value (50 kJ /M) of the ultimate energy that can be stored in a protein molecule without its destruction.
The existence of the critical value of binding energy should be accounted in structure based drug design methods where protein molecule is considered in an elastic deformation approximation.
Reference: accepted in Russian Journal of Biophysics, 2008
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